Every AI support agent takes input from someone it should not trust: the customer message, the documents it retrieves, and the tool calls it proposes. Koreshield screens all three before they become trusted model behavior or application execution. Data leaks, hidden instructions in help articles, policy drift and unsafe agent actions are checked before the model acts, and every decision is recorded. One call to integrate.
Today we are launching Koreshield. It sits between your AI support agent and every LLM.
Your support agent talks to strangers and acts on what they say. In most teams, nothing checks that input before the model acts on it. We do.
The problem: A support agent acts on inputs it cannot trust: the customer message, the documents it retrieves, and the tool calls it proposes. Our job is to check all three before they become trusted model behavior or application execution.
Two things you can try right now:
AI Security Gateway: One call before the model: your server sends the input to https://api.koreshield.com/v1/scan with an X-API-Key header, and the decision comes back before the model sees it. One declarative policy governs every request, and the same policy applies across OpenAI, Anthropic, Gemini and openai-compatible models, even though each provider normalises streaming and tool calls differently.
Tool action governance: Every proposed tool call is checked against your policy before it runs. A support bot steered toward an action it should not take simply does not execute, whether that is an account edit, an order change, or a refund above your limit.
Also part of the platform.:
- RAG Security checks the knowledge base the bot reads, so hidden instructions in a help article are caught.
- Every request and every block is recorded. The record matters as much as the block.
Honest about what this is.:
- Request-layer enforcement only. Response-layer inspection is on the roadmap.
- We are not claiming to have solved AI security. It is a new field and anyone who says otherwise is selling.
Free trial on the hosted plans (one-time 3-day trial, card required). Docs at docs.koreshield.ai, API key in about two minutes.
If you run an AI support agent, try to break it. If you do, we want the report first.
screening retrieved docs + tool calls not just the prompt is the right call 🔒 congrats!
this maps almost exactly onto the problem we deal with on the voice side - a transcribed customer call is just as untrusted an input as a chat message, arguably worse since background noise and misrecognized words create phrasing the screening layer has never seen before. curious how you tuned the false positive rate, since a support agent that gets blocked from doing a legitimate refund because the customer's wording pattern-matched something suspicious is its own kind of support failure
hi teslim, letting a support bot near real customers always made me a little uneasy, mostly the part where it'd refund someone by mistake. Having a gatekeeper check the move first, and log why it said no, calms that nerve. Nice ;)
Excited to see Koreshield live on Product Hunt🚀
The evidence side matters a lot to us. Being able to explain what an agent was allowed to do, what was blocked, and why.
We are building Koreshield to check customer messages, retrieved content and proposed tool calls before they lead to unsafe actions, with a record of each decision.
For anyone building AI support agents, what is your biggest concern about putting them into production? Would love your feedback.
About Koreshield on Product Hunt
“Security and evidence for AI support agents”
Koreshield launched on Product Hunt on September 23rd, 2026 and earned 91 upvotes and 11 comments, placing #14 on the daily leaderboard. Every AI support agent takes input from someone it should not trust: the customer message, the documents it retrieves, and the tool calls it proposes. Koreshield screens all three before they become trusted model behavior or application execution. Data leaks, hidden instructions in help articles, policy drift and unsafe agent actions are checked before the model acts, and every decision is recorded. One call to integrate.
Koreshield was featured in Developer Tools (521.5k followers), Artificial Intelligence (481.6k followers) and Security (3k followers) on Product Hunt. Together, these topics include over 226.7k products, making this a competitive space to launch in.
Who hunted Koreshield?
Koreshield was hunted by Teslim Kazeem. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Koreshield stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Teslim Kazeem